Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder

Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover its potential targets by bioinformatics methods. We first applied gene set variation analysis (GSVA) and seven different immune infiltration algorithms to the GSE98793 dataset to determine the differences in signaling pathways, metabolic pathways, and immune cell infiltration between MDD patients and healthy controls. Differentially expressed genes between MDD patients and controls were obtained from five datasets (GSE98793, GSE32280, GSE38206, GSE39653, and GSE52790), and 113 machine learning methods were employed to construct MDD diagnostic models. Based on the constructed MDD diagnostic models, MDD patients were divided into high-risk and low-risk groups. GSVA and immune microenvironment analyses were conducted to investigate the differences between the two groups. Furthermore, potential drugs and therapeutic targets for the high-risk MDD group were explored to provide new insights and directions for the precise treatment of MDD. GSVA and immune infiltration results indicate that patients with MDD exhibit differences from normal individuals in various aspects, including biological processes, signaling pathways, metabolic processes, and immune cells. To investigate the functions and biological significance of differentially expressed genes in MDD patients, we performed GO and KEGG enrichment analyses on the differentially expressed genes from five databases (GSE98793, GSE32280, GSE38206, GSE39653, and GSE52790). By comparing the enrichment results across the five datasets, we found that the cell-killing signaling pathway was consistently present in the enriched signaling pathways of all datasets, suggesting that this pathway may play a crucial role in the pathogenesis of MDD. The random forest algorithm (AUC = 0.788) was selected as the optimal algorithm from 113 machine learning algorithms, leading to the development of a robust and predictive MDD algorithm, highlighting the important role of NPL in MDD. By dividing MDD into high and low-risk subgroups based on diagnostic model scores, enrichment pathways, and immunological results further demonstrated that high-risk MDD is associated with increased levels of reactive oxygen species, inflammation, and numbers of T cells and B cells. Through GSEA scoring, five upregulated pathways in the high-risk MDD group were identified, and multiple potential drugs such as Mibefradil, LY364947, ZLN005, STA- 5326, and vemurafenib were screened. Patients with MDD show differences in signaling pathways, metabolic pathways, and immune mechanisms. By constructing an MDD diagnostic model, we predicted the key genes of MDD and the characteristic pathways associated with a higher risk of MDD. This provides new insights for risk stratification identification and offers new perspectives for the clinical application of precision immunotherapy and drug development.

Biological mechanism and functional verification of key genes related to major depressive disorder and type 2 diabetes mellitus

Major depressive disorder (MDD) and type 2 diabetes (T2D) have been shown to be linked, but a comprehensive understanding of the underlying molecular mechanisms remains elusive. The purpose of this study was to explore the biological relationship between MDD and T2D and verify the functional roles of key genes. We used the Gene Expression Omnibus database to investigate the targets associated with MDD and T2D. Using linear models for microarray data, differentially expressed genes associated with MDD and T2D were identified in GSE76826 and GSE95849, respectively, and 126 shared genes were significantly upregulated. Weighted gene coexpression network analysis identified modules associated with MDD and T2D in the GSE38206 and GSE20966 datasets and identified 8 common genes. Functional enrichment analysis revealed that these genes were enriched in cell signaling, enzyme activity, cell structure and amino acid biosynthesis and involved in cell death pathways. Finally, combined with the CTD and GeneCards databases, lysophosphatidylglycerol acyltransferase 1 (LPGAT1) was identified as a key gene. LPGAT1 was validated in GSE201332 and GSE182117, and the subject operating characteristic curve showed good diagnostic potential for MDD and T2D. Additionally, we used an in vitro model of MDD related to T2D to verify the expression of LPGAT1. A subsequent gene knockdown assay revealed that the downregulation of LPGAT1 improved mitochondrial function and reduced apoptosis in damaged neurons. Taken together, our results highlight the role of LPGAT1 in the connection between MDD and T2D, and these findings provide new insights into potential therapeutic targets for depression associated with diabetes.

Trends in research on novel antidepressant treatments

Mood disorders, such as major depressive disorder and bipolar disorder, are among the most common mental illnesses and a leading cause of disability worldwide. Key symptoms of these conditions include a depressed mood or anhedonia, sleep and psychomotor disturbances, changes in appetite or weight, and fatigue or loss of energy. Prolonged cognitive disturbances further impair the ability to think or concentrate and are often accompanied by persistent feelings of worthlessness or excessive guilt. Collectively, these symptoms underscore depression as a serious, long-term global health issue. In addition, clinical studies indicate a growing number of patients experiencing difficulties in responding to treatment, even in the long term. This phenomenon poses significant challenges for healthcare professionals, families, and patients alike. As a result, there is an urgent need for therapies that are both rapid-acting and safe. This review aims to summarize the prevailing trends in research on novel antidepressants, emphasizing their diversity and multi-directional mechanisms of action. The development of rapid-acting drugs is increasingly focused on achieving high efficacy, particularly for treatment-resistant depression. Such advances offer the potential for rapid therapeutic effects without the prolonged and often tedious administration of older generation antidepressants. Findings from studies using animal models of depression continue to play a crucial role in predicting and designing new therapeutic strategies. These models remain indispensable for understanding the physiological effects of newly developed compounds, thereby guiding the creation of innovative treatments.

Identifying the differentially expressed peripheral blood microRNAs in psychiatric disorders: a systematic review and meta-analysis

Background

Evidence has suggested that microRNAs (miRNAs) may play an important role in the pathogenesis of psychiatric disorders (PDs), but the results remain inconclusive. We aimed to identify specific differentially expressed miRNAs and their overlapping miRNA expression profiles in schizophrenia (SZ), major depression disorder (MDD), and bipolar disorder (BD), the three major PDs.

Methods

The literatures up to September 30, 2023 related to peripheral blood miRNAs and PDs were searched and screened from multiple databases. The differences in miRNA levels between groups were illustrated by the standardized mean difference (SMD) and 95% confidence interval (95% CI).

Results

In total, 30 peripheral blood miRNAs were included in the meta-analysis, including 16 for SZ, 12 for MDD, and 2 for BD, each was reported in more than 3 independent studies. Compared with the control group, miR-181b-5p, miR-34a-5p, miR-195-5p, miR-30e-5p, miR-7-5p, miR-132-3p, miR-212-3p, miR-206, miR-92a-3p and miR-137-3p were upregulated in SZ, while miR-134-5p, miR-107 and miR-99b-5p were downregulated. In MDD, miR-124-3p, miR-132-3p, miR-139-5p, miR-182-5p, miR-221-3p, miR-34a-5p and miR-93-5p were upregulated, while miR-144-5p and miR-135a-5p were downregulated. However, we failed to identify statistically differentially expressed miRNAs in BD. Interestingly, miR-132-3p and miR-34a-5p were upregulated in both SZ and MDD.

Conclusions

Our study identified 13 differentially expressed miRNAs in SZ and 9 in MDD, among which miR-132-3p and miR-34a-5p were upregulated in both SZ and MDD by systematically analyzing qualified studies. These miRNAs may be used as potential biomarkers for the diagnosis of SZ and MDD in the future.

Systematic Review Registration

http://www.crd.york.ac.uk/PROSPERO, identifier CRD42023486982.

miRNAs in treatment-resistant depression: a systematic review

BACKGROUND

Treatment-resistant depression (TRD) is a condition in a subset of depressed patients characterized by resistance to antidepressant medications. The global prevalence of TRD has been steadily increasing, yet significant advancements in its diagnosis and treatment remain elusive despite extensive research efforts. The precise underlying pathogenic mechanisms are still not fully understood. Epigenetic mechanisms play a vital role in a wide range of diseases. In recent years, investigators have increasingly focused on the regulatory roles of miRNAs in the onset and progression of TRD. miRNAs are a class of noncoding RNA molecules that regulate the translation and degradation of their target mRNAs via interaction, making the exploration of their functions in TRD essential for elucidating their pathogenic mechanisms.

METHODS AND RESULTS

A systematic search was conducted in four databases, namely PubMed, Web of Science, Cochrane Library, and Embase, focusing on studies related to treatment-resistant depression and miRNAs. The search was performed using terms individually or in combination, such as "treatment-resistant depression," "medication-resistant depression," and "miRNAs." The selected articles were reviewed and collated, covering the time period from the inception of each database to the end of February 2024. We found that miRNAs play a crucial role in the pathophysiology of TRD through three main aspects: 1) involvement in miRNA-mediated inflammatory responses (including miR-155, miR-345-5p, miR-146a, and miR-146a-5p); 2) influence on 5-HT transport processes (including miR-674,miR-708, and miR-133a); and 3) regulation of synaptic plasticity (including has-miR-335-5p,has-miR- 1292-3p, let-7b, and let-7c). Investigating the differential expression and interactions of these miRNAs could contribute to a deeper understanding of the molecular mechanisms underlying TRD.

CONCLUSIONS

miRNAs might play a pivotal role in the pathogenesis of TRD. Gaining a deeper understanding of the roles and interrelations of miRNAs in TRD will contribute to elucidating disease pathogenesis and potentially provide avenues for the development of novel diagnostic and therapeutic strategies.

Clinical Insights into MicroRNAs in Depression: Bridging Molecular Discoveries and Therapeutic Potential

Depression is a major contributor to the overall global burden of disease. The discovery of biomarkers for diagnosis or prediction of treatment responses and as therapeutic agents is a current priority. Previous studies have demonstrated the importance of short RNA molecules in the etiology of depression. The most extensively researched of these are microRNAs, a major component of cellular gene regulation and function. MicroRNAs function in a temporal and tissue-specific manner to regulate and modify the post-transcriptional expression of target mRNAs. They can also be shuttled as cargo of extracellular vesicles between the brain and the blood, thus informing about relevant mechanisms in the CNS through the periphery. In fact, studies have already shown that microRNAs identified peripherally are dysregulated in the pathological phenotypes seen in depression. Our article aims to review the existing evidence on microRNA dysregulation in depression and to summarize and evaluate the growing body of evidence for the use of microRNAs as a target for diagnostics and RNA-based therapies.

MicroRNAs as Diagnostic Biomarkers and Predictors of Antidepressant Response in Major Depressive Disorder: A Systematic Review

Despite the hardships of major depressive disorder (MDD), biomarkers for the diagnosis and pharmacological management of this condition are lacking. MicroRNAs are epigenetic mechanisms that could provide promising MDD biomarkers. Our aim was to summarize the findings and provide validation for the selection and use of specific microRNAs as biomarkers in the diagnosis and treatment of MDD. A systematic review was conducted using the PubMed/Medline, Cochrane, PsycINFO, Embase, and LILACS databases from March 2022 to November 2023, with clusters of terms based on "microRNA" and "antidepressant". Studies involving human subjects, animal models, and cell cultures were included, whereas those that evaluated herbal medicines, non-pharmacological therapies, or epigenetic mechanisms other than miRNA were excluded. The review revealed differences in the expression of various microRNAs when considering the time of assessment (before or after antidepressant treatment) and the population studied. However, due to the heterogeneity of the microRNAs investigated, the limited size of the samples, and the wide variety of antidepressants used, few conclusions could be made. Despite the observed heterogeneity, the following microRNAs were determined to be important factors in MDD and the antidepressant response: mir-1202, mir-135, mir-124, and mir-16. The findings indicate the potential for the use of microRNAs as biomarkers for the diagnosis and treatment of MDD; however, more homogeneous studies are needed.

Elevated Expression of HSP72 in the Prefrontal Cortex and Hippocampus of Rats Subjected to Chronic Mild Stress and Treated with Imipramine

The HSP70 and HSP90 family members belong to molecular chaperones that exhibit protective functions during the cellular response to stressful agents. We investigated whether the exposure of rats to chronic mild stress (CMS), a validated model of depression, affects the expression of HSP70 and HSP90 in the prefrontal cortex (PFC), hippocampus (HIP) and thalamus (Thal). Male Wistar rats were exposed to CMS for 3 or 8 weeks. The antidepressant imipramine (IMI, 10 mg/kg, i.p., daily) was introduced in the last five weeks of the long-term CMS procedure. Depressive-like behavior was verified by the sucrose consumption test. The expression of mRNA and protein was quantified by real-time PCR and Western blot, respectively. In the 8-week CMS model, stress alone elevated HSP72 and HSP90B mRNA expression in the HIP. HSP72 mRNA was increased in the PFC and HIP of rats not responding to IMI treatment vs. IMI responders. The CMS exposure increased HSP72 protein expression in the cytosolic fraction of the PFC and HIP, and this effect was diminished by IMI treatment. Our results suggest that elevated levels of HSP72 may serve as an important indicator of neuronal stress reactions accompanying depression pathology and could be a potential target for antidepressant strategy.

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Background

Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets.

Method

A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array.

Results

Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder.

Conclusion

Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.

Down-regulated miR-16-2 in peripheral blood is positively correlated with decreased bilateral insula volume in patients with major depressive disorder

BACKGROUND

The downregulated microRNA-16-2-3p (miR-16-2) had been believed to be associated with major depressive disorder (MDD). This study aimed to investigate the potential of miR-16-2 as a biomarker for MDD by analysing its expression levels, furthermore, to explore the relationship between miR-16-2, clinical symptoms and alterations in grey matter volume (GMV) in MDD patients.

METHODS

Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression level of miR-16-2 in 48 drug-naïve patients with MDD and 50 healthy controls (HCs). We conducted ROC curve analysis to assess the diagnostic value of miR-16-2 in MDD, and evaluated its ability to predict antidepressant response by reassessing depressive and anxiety symptoms after treatment. Voxel-based morphometry was carried out to explore alterations in regional GMV that may be associated with MDD. Pearson analysis was used to explore the relationship between miR-16-2 expression, clinical symptoms, and altered GMV in the brains of MDD patients.

RESULTS

We found that MDD patients had significantly downregulated miR-16-2 expression, which was negatively correlated with HAMD-17 and HAMA-14 scores, and had great diagnostic value for MDD (AUC = 0.806, 95 % CI: 0.721-0.891). In addition, MDD patients had significantly lower GMV in the bilateral insula and left superior temporal gyrus (STG_L) than HCs. GMV reduction in the bilateral insula was found to be correlated with miR-16-2 expression.

CONCLUSIONS

Our findings support the potential value of miRNA-16-2 as a biomarker for MDD. It also suggests that miRNA-16-2 may be associated with abnormal insula and involved in pathophysiological mechanisms of MDD.

Association between gene methylation and experiences of historical trauma in Alaska Native peoples

BACKGROUND

Historical trauma experienced by Indigenous peoples of North America is correlated with health disparities and is hypothesized to be associated with DNA methylation. Massive group traumas such as genocide, loss of land and foodways, and forced conversion to Western lifeways may be embodied and affect individuals, families, communities, cultures, and health. This study approaches research with Alaska Native people using a community-engaged approach designed to create mutually-beneficial partnerships, including intentional relationship development, capacity building, and sample and data care.

METHODS

A total of 117 Alaska Native individuals from two regions of Alaska joined the research study. Participants completed surveys on cultural identification, historical trauma (historical loss scale and historical loss associated symptoms scale), and general wellbeing. Participants provided a blood sample which was used to assess DNA methylation with the Illumina Infinium MethylationEPIC array.

RESULTS

We report an association between historical loss associated symptoms and DNA methylation at five CpG sites, evidencing the embodiment of historical trauma. We further report an association between cultural identification and general wellbeing, complementing evidence from oral narratives and additional studies that multiple aspects of cultural connection may buffer the effects of and/or aid in the healing process from historical trauma.

CONCLUSION

A community-engaged approach emphasizes balanced partnerships between communities and researchers. Here, this approach helps better understand embodiment of historical trauma in Alaska Native peoples. This analysis reveals links between the historical trauma response and DNA methylation. Indigenous communities have been stigmatized for public health issues instead caused by systemic inequalities, social disparities, and discrimination, and we argue that the social determinants of health model in Alaska Native peoples must include the vast impact of historical trauma and ongoing colonial violence.

Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential

Major depressive disorder (MDD) is a chronic, generally episodic and debilitating disease that affects an estimated 300 million people worldwide, but its pathogenesis is poorly understood. The heritability estimate of MDD is 30-40%, suggesting that genetics alone do not account for most of the risk of major depression. Another factor known to associate with MDD involves environmental stressors such as childhood adversity and recent life stress. Recent studies have emerged to show that the biological impact of environmental factors in MDD and other stress-related disorders is mediated by a variety of epigenetic modifications. These epigenetic modification alterations contribute to abnormal neuroendocrine responses, neuroplasticity impairment, neurotransmission and neuroglia dysfunction, which are involved in the pathophysiology of MDD. Furthermore, epigenetic marks have been associated with the diagnosis and treatment of MDD. The evaluation of epigenetic modifications holds promise for further understanding of the heterogeneous etiology and complex phenotypes of MDD, and may identify new therapeutic targets. Here, we review preclinical and clinical epigenetic findings, including DNA methylation, histone modification, noncoding RNA, RNA modification, and chromatin remodeling factor in MDD. In addition, we elaborate on the contribution of these epigenetic mechanisms to the pathological trait variability in depression and discuss how such mechanisms can be exploited for therapeutic purposes.

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Subject

Major depressive disorder (MDD) negatively affects patients' behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD.

Methods

Gene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes.

Results

A diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.

Conclusion

Our current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD.

Endocannabinoid signaling and epigenetics modifications in the neurobiology of stress-related disorders

Stress exposure is associated with psychiatric conditions, such as depression, anxiety, and post-traumatic stress disorder (PTSD). It is also a vulnerability factor to developing or reinstating substance use disorder. Stress causes several changes in the neuro-immune-endocrine axis, potentially resulting in prolonged dysfunction and diseases. Changes in several transmitters, including serotonin, dopamine, glutamate, gamma-aminobutyric acid (GABA), glucocorticoids, and cytokines, are associated with psychiatric disorders or behavioral alterations in preclinical studies. Complex and interacting mechanisms make it very difficult to understand the physiopathology of psychiatry conditions; therefore, studying regulatory mechanisms that impact these alterations is a good approach. In the last decades, the impact of stress on biology through epigenetic markers, which directly impact gene expression, is under intense investigation; these mechanisms are associated with behavioral alterations in animal models after stress or drug exposure, for example. The endocannabinoid (eCB) system modulates stress response, reward circuits, and other physiological functions, including hypothalamus-pituitary-adrenal axis activation and immune response. eCBs, for example, act retrogradely at presynaptic neurons, limiting the release of neurotransmitters, a mechanism implicated in the antidepressant and anxiolytic effects after stress. Epigenetic mechanisms can impact the expression of eCB system molecules, which in turn can regulate epigenetic mechanisms. This review will present evidence of how the eCB system and epigenetic mechanisms interact and the consequences of this interaction in modulating behavioral changes after stress exposure in preclinical studies or psychiatric conditions. Moreover, evidence that correlates the involvement of the eCB system and epigenetic mechanisms in drug abuse contexts will be discussed.

Decreased miR-451a in cerebrospinal fluid, a marker for both cognitive impairment and depressive symptoms in Alzheimer's disease

Background: Alzheimer's disease (AD) patients are often accompanied by depressive symptoms, but its underlying mechanism remains unclear. The present study aimed to explore the potential role of microRNAs in the comorbidity of AD and depression. Methods: The miRNAs associated with AD and depression were screened from databases and literature and then confirmed in the cerebrospinal fluid (CSF) of AD patients and different ages of transgenic APP/PS1 mice. AAV9-miR-451a-GFP was injected into the medial prefrontal cortex (mPFC) of APP/PS1 mice at seven months, and four weeks later, a series of behavioral and pathological analyses were performed. Results: AD patients had low CSF levels of miR-451a, which was positively correlated with the cognitive assessment score, but negatively with their depression scale. In the mPFC of APP/PS1 transgenic mice, the miR-451a levels also decreased significantly in the neurons and microglia. Specific virus vector-induced overexpression of miR-451a in the mPFC of APP/PS1 mice ameliorated AD-related behavior deficits and pathologies, including long-term memory defects, depression-like phenotype, β-amyloid load, and neuroinflammation. Mechanistically, miR-451a decreased the expression of neuronal β-secretase 1 of neurons through inhibiting Toll-like receptor 4/Inhibitor of kappa B Kinase β/ Nuclear factor kappa-B signaling pathway and microglial activation by inhibiting activation of NOD-like receptor protein 3, respectively. Conclusion: This finding highlighted miR-451a as a potential target for diagnosing and treating AD, especially for those with coexisting symptoms of depression.

Aberrant Histone Modification of TNFAIP3, TLR4, TNIP2, miR-146a, and miR-155 in Major Depressive Disorder

Activated toll-like receptor (TLR) signaling has been well investigated in major depressive disorder (MDD). We previously reported that TNFAIP3, TLR4, TNIP2, miR-146a, and miR-155 play important roles in regulating the toll-like receptor 4 (TLR4) signaling pathway and may serve as novel targets in the pathogenesis of MDD. Recently, aberrant histone modification has been implicated in several psychiatric disorders, including schizophrenia and mood disorder; the most thoroughly studied modification is histone 3 lysine 4 tri-methylation (H3K4me3). In this work, we aimed to explore H3K4me3 differences in the promotors of genes encoding the abovementioned factors in patients with MDD, and whether they were altered after antidepressant treatment. A total of 30 MDD patients and 28 healthy controls were recruited. Peripheral blood mononuclear cells (PBMCs) were collected. The levels of H3K4me3 in the promoters of TNFAIP3, TLR4, TNIP2, miR-146a, and miR-155 were measured through chromatin immunoprecipitation (ChIP) followed by DNA methylation assay. Analysis of covariance was used to evaluate between-group differences after adjusting for age, sex, BMI, and smoking. In comparison with healthy controls, patients with MDD showed significantly lower H3K4me3 levels in the promoters of TNFAIP3, TLR4, TNIP2, miR-146a, and miR-155 in PBMCs. These levels were not significantly altered after completion of a 4-week antidepressant treatment. To explore the association between depression severity and H3K4me3 levels, a multiple linear regression model was generated. The results revealed that levels of H3K4me3 in the TNIP2 promoters a negative correlation with the 17-item Hamilton Depression Rating Scale (HAND-17) score, whereas that of TLR4 had a positive correlation with this score. The present results suggest that decreased H3K4me3 levels in the promoters of the genes encoding TNFAIP3, TLR4, miR-146a, miR-155, and TNIP2 are involved in psychopathology of major depressive disorder.

MiR-1281 is involved in depression disorder and the antidepressant effects of Kai-Xin-San by targeting ADCY1 and DVL1

Kai-Xin-San (KXS) is a Chinese medicine formulation that is commonly used to treat depression caused by dual deficiencies in the heart and spleen. Recent studies indicated that miRNAs were involved in the pathophysiology of depression. However, there have been few studies on the mechanism underlying the miRNAs directly mediating antidepressant at clinical level, especially in nature drugs and TCM compound. In this study, we identified circulating miRNAs defferentially expressed among the depression patients (DPs), DPs who underwent 8weeks of KXS treatment and health controls (HCs). A total of 45 miRNAs (17 were up-regulated and 28 were down-regulated) were significantly differentially expressed among three groups. Subsequently, qRT-PCR was used to verify 10 differentially expressed candidate miRNAs in more serum samples, and the results showed that 6 miRNAs (miR-1281, miR-365a-3p, miR-2861, miR-16-5p, miR-1202 and miR-451a) were consistent with the results of microarray. Among them, miR-1281, was the novel dynamically altered and appeared to be specifically related to depression and antidepressant effects of KXS. MicroRNA-gene-pathway-net analysis showed that miR-1281-regulated genes are mostly key nodes in the classical signaling pathway related to depression. Additionally, our data suggest that ADCY1 and DVL1 were the targets of miR-1281. Thus, based on the discovery of miRNA expression profiles in vivo, our findings suggest a new role for miR-1281 related to depression and demonstrated in vitro that KXS may activate cAMP/PKA/ERK/CREB and Wnt/β-catenin signal transduction pathways by down-regulating miR-1281 that targets ADCY1 and DVL1 to achieve its role in neuronal cell protection.

A bioinformatics approach to identifying the biomarkers and pathogenesis of major depressive disorder combined with acute myocardial infarction

This study investigated the pathogenesis of major depressive disorder (MDD) and acute myocardial infarction (AMI) using bioinformatics. We analyzed MDD and AMI (MDD-AMI) datasets provided by the Gene Expression Omnibus (GEO) database for genes common to MDD and AMI using GEO2R and weighted gene co-expression network analysis (WGCNA). We also performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and we used Disease Ontology (DO) analysis to identify a) the pathways through which genes function and b) comorbidities. We also created a protein-protein interaction (PPI) network using the STRING database to identify the hub genes and biomarkers. NetworkAnalyst 3.0 was used to construct a transcription factor (TF) gene regulatory network. We also identified relevant complications and potential drug candidates. The 27 genes common to MDD and AMI were enriched in the pathways regulating TFs and mediating immunity and inflammation. The hub genes in the PPI network included TLR2, HP, ICAM1, LCN2, LTF, VCAN, S100A9 and NFKBIA. Key TFs were KLF9, KLF11, ZNF24, and ZNF580. Cardiovascular, pancreatic, and skeletal diseases were common complications. Hydrocortisone, simvastatin, and estradiol were candidate treatment drugs. Identification of these genes and their pathways may provide new targets for further research on the pathogenesis, biomarkers, and treatment of MDD-AMI. Together our results suggested that TLR2 and VCAN might be the key genes associated with MDD complicated by AMI.

Role of the Intestinal Microbiota in the Genesis of Major Depression and the Response to Antidepressant Drug Therapy: A Narrative Review

A major depressive disorder is a serious mental illness characterized by a pervasive low mood that negatively concerns personal life, work life, or education, affecting millions of people worldwide. To date, due to the complexity of the disease, the most common and effective treatments consist of a multi-therapy approach, including psychological, social, and pharmacological support with antidepressant drugs. In general, antidepressants are effective in correcting chemical imbalances of neurotransmitters in the brain, but recent evidence has underlined the pivotal role of gut microbiota (GM) also in the regulation of their pharmacokinetics/pharmacodynamics, through indirect or direct mechanisms. The study of these complex interactions between GM and drugs is currently under the spotlight, and it has been recently named "pharmacomicrobiomics". Hence, the purpose of this review is to summarize the contribution of GM and its metabolites in depression, as well as their role in the metabolism and activity of antidepressant drugs, in order to pave the way for the personalized administration of antidepressant therapies.

Habenula as a Possible Target for Treatment-Resistant Depression Phenotype in Wistar Kyoto Rats

The mechanisms of treatment-resistant depression (TRD) are not clear and are difficult to study. An animal model resembling human TRD is the Wistar Kyoto rat strain. In the present study, we focused on selecting miRNAs that differentiate rats of the WKY strain from Wistar Han (WIS) rats in two divisions of the habenula, the lateral and medial (LHb and MHb, respectively). Based on our preliminary study and literature survey, we identified 32 miRNAs that could be potentially regulated in the habenula. Six miRNAs significantly differentiated WKY rats from WIS rats within the MHb, and three significantly differentiated WKY from WIS rats within the LHb. Then, we selected relevant transcripts regulated by those miRNAs, and their expression in the habenular nuclei was investigated. For mRNAs that differentiated WKY rats from WIS rats in the MHb (Cdkn1c, Htr7, Kcnj9, and Slc12a5), their lower expression correlated with a higher level of relevant miRNAs. In the LHb, eight mRNAs significantly differentiated WKY from WIS rats (upregulated Htr4, Drd2, Kcnj5, and Sstr4 and downregulated Htr2a, Htr7, Elk4, and Slc12a5). These data indicate that several important miRNAs are expressed in the habenula, which differentiates WKY rats from WIS rats and in turn correlates with alterations in the expression of target transcripts. Of particular note are two genes whose expression is altered in WKY rats in both LHb and MHb: Slc12a5 and Htr7. Regulation of KCC2 via the 5-HT7 receptor may be a potential target for the treatment of TRD.

Dissecting early life stress-induced adolescent depression through epigenomic approach

Early life stress (ELS), such as abuse and neglect during childhood, can lead to psychiatric disorders in later life. Previous studies have suggested that ELS can cause profound changes in gene expression through epigenetic mechanisms, which can lead to psychiatric disorders in adulthood; however, studies on epigenetic modifications associated with ELS and psychiatric disorders in adolescents are limited. Moreover, how these epigenetic modifications can lead to psychiatric disorders in adolescents is not fully understood. Commonly, DNA methylation, histone modification, and the regulation of noncoding RNAs have been attributed to the reprogramming of epigenetic profiling associated with ELS. Although only a few studies have attempted to examine epigenetic modifications in adolescents with ELS, existing evidence suggests that there are commonalities and differences in epigenetic profiling between adolescents and adults. In addition, epigenetic modifications are sex-dependent and are influenced by the type of ELS. In this review, we have critically evaluated the current evidence on epigenetic modifications in adolescents with ELS, particularly DNA methylation and the expression of microRNAs in both preclinical models and humans. We have also clarified the impact of ELS on psychiatric disorders in adolescents to predict the development of neuropsychiatric disorders and to prevent and recover these disorders through personalized medicine.

The autism risk factor CHD8 is a chromatin activator in human neurons and functionally dependent on the ERK-MAPK pathway effector ELK1

The chromodomain helicase DNA-binding protein CHD8 is the most frequently mutated gene in autism spectrum disorder. Despite its prominent disease involvement, little is known about its molecular function in the human brain. CHD8 is a chromatin regulator which binds to the promoters of actively transcribed genes through genomic targeting mechanisms which have yet to be fully defined. By generating a conditional loss-of-function and an endogenously tagged allele in human pluripotent stem cells, we investigated the molecular function and the interaction of CHD8 with chromatin in human neurons. Chromatin accessibility analysis and transcriptional profiling revealed that CHD8 functions as a transcriptional activator at its target genes in human neurons. Furthermore, we found that CHD8 chromatin targeting is cell context-dependent. In human neurons, CHD8 preferentially binds at ETS motif-enriched promoters. This enrichment is particularly prominent on the promoters of genes whose expression significantly changes upon the loss of CHD8. Indeed, among the ETS transcription factors, we identified ELK1 as being most highly correlated with CHD8 expression in primary human fetal and adult cortical neurons and most highly expressed in our stem cell-derived neurons. Remarkably, ELK1 was necessary to recruit CHD8 specifically to ETS motif-containing sites. These findings imply that ELK1 and CHD8 functionally cooperate to regulate gene expression and chromatin states at MAPK/ERK target genes in human neurons. Our results suggest that the MAPK/ERK/ELK1 axis potentially contributes to the pathogenesis caused by CHD8 mutations in human neurodevelopmental disorders.

Heterogeneity in major depressive disorder: The need for biomarker-based personalized treatments

Major Depressive Disorder (MDD) or depression is a pathological mental condition affecting millions of people worldwide. Identification of objective biological markers of depression can provide for a better diagnostic and intervention criteria; ultimately aiding to reduce its socioeconomic health burden. This review provides a comprehensive insight into the major biomarker candidates that have been implicated in depression neurobiology. The key biomarker categories are covered across all the "omics" levels. At the epigenomic level, DNA-methylation, non-coding RNA and histone-modifications have been discussed in relation to depression. The proteomics system shows great promise with inflammatory markers as well as growth factors and neurobiological alterations within the endocannabinoid system. Characteristic lipids implicated in depression together with the endocrine system are reviewed under the metabolomics section. The chapter also examines the novel biomarkers for depression that have been proposed by studies in the microbiome. Depression affects individuals differentially and explicit biomarkers identified by robust research criteria may pave the way for better diagnosis, intervention, treatment, and prediction of treatment response.

Salivary microRNA and Metabolic Profiles in a Mouse Model of Subchronic and Mild Social Defeat Stress

Identification of early biomarkers of stress is important for preventing mood and anxiety disorders. Saliva is an easy-to-collect and non-invasive diagnostic target. The aim of this study was to characterize the changes in salivary whole microRNAs (miRNAs) and metabolites in mice subjected to subchronic and mild social defeat stress (sCSDS). In this study, we identified seven upregulated and one downregulated miRNAs/PIWI-interacting RNA (piRNA) in the saliva of sCSDS mice. One of them, miR-208b-3p, which is reported as a reliable marker for myocardial infarction, was upregulated in the saliva of sCSDS mice. Histological analysis showed frequent myocardial interstitial fibrosis in the heart of such mice. In addition, gene ontology and pathway analyses suggested that the pathways related to energy metabolism, such as the oxidative phosphorylation and the pentose phosphate pathway, were significantly related to the miRNAs affected by sCSDS in saliva. In contrast, salivary metabolites were not significantly changed in the sCSDS mice, which is consistent with our previous metabolomic study on the plasma of sCSDS mice. Taken in the light of previous studies, the present study provides novel potential stress biomarkers for future diagnosis using saliva.

MicroRNAs, Stem Cells in Bipolar Disorder, and Lithium Therapeutic Approach

Bipolar disorder (BD) is a severe, chronic, and disabling neuropsychiatric disorder characterized by recurrent mood disturbances (mania/hypomania and depression, with or without mixed features) and a constellation of cognitive, psychomotor, autonomic, and endocrine abnormalities. The etiology of BD is multifactorial, including both biological and epigenetic factors. Recently, microRNAs (miRNAs), a class of epigenetic regulators of gene expression playing a central role in brain development and plasticity, have been related to several neuropsychiatric disorders, including BD. Moreover, an alteration in the number/distribution and differentiation potential of neural stem cells has also been described, significantly affecting brain homeostasis and neuroplasticity. This review aimed to evaluate the most reliable scientific evidence on miRNAs as biomarkers for the diagnosis of BD and assess their implications in response to mood stabilizers, such as lithium. Neural stem cell distribution, regulation, and dysfunction in the etiology of BD are also dissected.

miRNAs as potential diagnostic biomarkers and pharmacogenomic indicators in psychiatric disorders

The heterogeneity of psychiatric disorders and the lack of reliable biomarkers for prediction and treatments follow-up pose difficulties towards recognition and understanding of the molecular basis of psychiatric diseases. However, several studies based on NGS approaches have shown that miRNAs could regulate gene expression during onset and disease progression and could serve as potential diagnostic and pharmacogenomics biomarkers during treatment. We provide herein a detailed overview of circulating miRNAs and their expression profiles as biomarkers in schizophrenia, bipolar disorder and major depressive disorder and their role in response to specific treatments. Bioinformatics analysis of miR-34a, miR-106, miR-134 and miR-132, which are common among SZ, BD and MDD patients, showed brain enrichment and involvement in the modulation of critical signaling pathways, which are often deregulated in psychiatric disorders. We propose that specific miRNAs support accurate diagnosis and effective precision treatment of psychiatric disorders.

Cellular and immunometabolic mechanisms of inflammation in depression: Preliminary findings from single cell RNA sequencing and a tribute to Bruce McEwen

Inflammation is associated with symptoms of anhedonia, a core feature of major depression (MD). We have shown that MD patients with high inflammation as measured by plasma C-reactive protein (CRP) and anhedonia display gene signatures of metabolic reprograming (e.g., shift to glycolysis) necessary to sustain cellular immune activation. To gain preliminary insight into the immune cell subsets and transcriptomic signatures that underlie increased inflammation and its relationship with behavior in MD at the single-cell (sc) level, herein we conducted scRNA-Seq on peripheral blood mononuclear cells from a subset of medically-stable, unmedicated MD outpatients. Three MD patients with high CRP (>3 mg/L) before and two weeks after anti-inflammatory challenge with the tumor necrosis factor antagonist infliximab and three patients with low CRP (≤3 mg/L) were studied. Cell clusters were identified using a Single Cell Wizard pipeline, followed by pathway analysis. CD14⁺ and CD16⁺ monocytes were more abundant in MD patients with high CRP and were reduced by 29% and 55% respectively after infliximab treatment. Within CD14⁺ and CD16⁺ monocytes, genes upregulated in high CRP patients were enriched for inflammatory (phagocytosis, complement, leukocyte migration) and immunometabolic (hypoxia-inducible factor [HIF]-1, aerobic glycolysis) pathways. Shifts in CD4⁺ T cell subsets included ∼30% and ∼10% lower abundance of CD4⁺ central memory (T_CM) and naïve cells and ∼50% increase in effector memory-like (T_EM-like) cells in high versus low CRP patients. T_CM cells of high CRP patients displayed downregulation of the oxidative phosphorylation (OXPHOS) pathway, a main energy source in this cell type. Following infliximab, changes in the number of CD14⁺ monocytes and CD4⁺ T_EM-like cells predicted improvements in anhedonia scores (r = 1.0, p < 0.001). In sum, monocytes and CD4⁺ T cells from MD patients with increased inflammation exhibited immunometabolic reprograming in association with symptoms of anhedonia. These findings are the first step toward determining the cellular and molecular immune pathways associated with inflammatory phenotypes in MD, which may lead to novel immunomodulatory treatments of psychiatric illnesses with increased inflammation.

A New Player in Depression: MiRNAs as Modulators of Altered Synaptic Plasticity

Depression is a psychiatric disorder that presents with a persistent depressed mood as the main clinical feature and is accompanied by cognitive impairment. Changes in neuroplasticity and neurogenesis greatly affect depression. Without genetic changes, epigenetic mechanisms have been shown to function by regulating gene expression during the body’s adaptation to stress. Studies in recent years have shown that as important regulatory factors in epigenetic mechanisms, microRNAs (miRNAs) play important roles in the development and progression of depression through the regulation of protein expression. Herein, we review the mechanisms of miRNA-mediated neuroplasticity in depression and discus synaptic structural plasticity, synaptic functional plasticity, and neurogenesis. Furthermore, we found that miRNAs regulate neuroplasticity through several signalling pathways to affect cognitive functions. However, these pathways do not work independently. Therefore, we try to identify synergistic correlations between miRNAs and multiple signalling pathways to broaden the potential pathogenesis of depression. In addition, in the future, dual-function miRNAs (protection/injury) are promising candidate biomarkers for the diagnosis of depression, and their regulated genes can potentially be used as target genes for the treatment of depression.

Multiple Pre-Treatment miRNAs Levels in Untreated Major Depressive Disorder Patients Predict Early Response to Antidepressants and Interact with Key Pathways

Major depressive disorder (MDD) is a life-impairing disorder, and early successful treatment is important for a favorable prognosis. However, early response to antidepressants differs widely among individuals, and is difficult to predict pre-treatment. As miRNAs have been reported to play important roles in depression, identification of miRNAs associated with antidepressant treatment responses and their interacting genes and pathways will be beneficial in understanding the predictors and molecular mechanisms of depression treatment. This randomized control trial examined miRNAs correlated with the early therapeutic effect of selective serotonin reuptake inhibitors (SSRIs; paroxetine or sertraline) and mirtazapine monotherapy. Before medication, we comprehensively analyzed the miRNA expression of 92 depressed participants and identified genes and pathways interacting with miRNAs. A total of 228 miRNAs were significantly correlated with depressive symptoms improvements after 2 weeks of SSRIs treatment, with miR-483.5p showing the most robust correlation. These miRNAs are involved in 21 pathways, including TGF-β, glutamatergic synapse, long-term depression, and the mitogen-activated protein kinase (MAPK) signaling pathways. Using these miRNAs enabled us to predict SSRI response at week 2 with a 57% difference. This study shows that pre-treatment levels of miRNAs could be used to predict early responses to antidepressant administration, a knowledge of genes, and an identification of genes and pathways associated with the antidepressant response.

Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents

Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

Whole blood transcriptional signatures associated with rapid antidepressant response to ketamine in patients with treatment resistant depression

Ketamine has rapid and sustained antidepressant effects in patients with treatment-resistant depression (TRD). However, the underlying mechanisms of action are not well understood. There is increasing evidence that TRD is associated with a pro-inflammatory state and that ketamine may inhibit inflammatory processes. We thus investigated whole blood transcriptional profiles related to TRD and gene expression changes associated with treatment response to ketamine. Whole blood was collected at baseline (21 healthy controls [HC], 26 patients with TRD) and then again in patients with TRD 24 hours following a single intravenous infusion of ketamine (0.5 mg/kg). We performed RNA-sequencing and analyzed (a) baseline transcriptional profiles between patients with TRD and HC, (b) responders vs. non-responders before ketamine treatment, and (c) gene expression signatures associated with clinical improvement. At baseline, patients with TRD compared to HC showed a gene expression signature indicative of interferon signaling pathway activation. Prior to ketamine administration, the metabotropic glutamate receptor gene GRM2 and the ionotropic glutamate receptor gene GRIN2D were upregulated in responders compared to non-responders. Response to ketamine was associated with a distinct transcriptional signature, however, we did not observe gene expression changes indicative of an anti-inflammatory effect. Future studies are needed to determine the role of the peripheral immune system in the antidepressant effect of ketamine.

Mapping the structure of depression biomarker research: A bibliometric analysis

BACKGROUND

Depression is a common mental disorder and the diagnosis is still based on the descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis, prognosis, and treatment. In recent years, many biomarkers relevant to the mechanisms of depression have been identified. This study uses bibliometric methods and visualization tools to analyse the literature on depression biomarkers and its hot topics, and research frontiers to provide references for future research.

METHODS

Scientific publications related to depression biomarkers published between 2009 and 2022 were obtained from the Web of Science database. The BICOMB software was used to extract high-frequency keywords and to construct binary word-document and co-word matrices. gCLUTO was used for bicluster and visual analyses of high-frequency keywords. Further graphical visualizations were generated using R, CiteSpace and VOSviewer software.

RESULTS

A total of 14,403 articles related to depression biomarkers were identified. The United States (34.81%) and China (15.68%), which together account for more than half of all publications, can be considered the research base for the field. Among institutions, the University of California, University of London, and Harvard University are among the top in terms of publication number. Three authors (Maes M, Penninx B.W.J.H., and Berk M) emerged as eminent researchers in the field. Finally, eight research hotspots for depression biomarkers were identified using reference co-citation analysis.

CONCLUSION

This study used bibliometric methods to characterize the body of literature and subject knowledge in the field of depression biomarker research. Among the core biomarkers of depression, functional magnetic resonance imaging (fMRI), cytokines, and oxidative stress are relatively well established; however, research on machine learning, metabolomics, and microRNAs holds potential for future development. We found "microRNAs" and "gut microbiota" to be the most recent burst terms in the study of depression biomarkers and the likely frontiers of future research.

In silico-labeled ghost cytometry

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

Association of circulating let-7b-5p with major depressive disorder: a nested case-control study

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders and is a great disease burden. However, its underlying pathophysiology and aetiology remain poorly understood. Available evidence suggests that circulating microRNAs (miRNAs) are associated with MDD, but it is still unknown whether miRNAs can predict subsequent incident MDD.

METHODS

In this nested case-control study, a total of 104 individuals, who were free of MDD at baseline, from the Women's Health in Lund Area (WHILA) cohort were included. Among them, 52 individuals developed MDD (cases) during the 5 years follow-up and 52 individuals did not develop MDD (controls). Plasma expression levels of miR-17-5p, miR-134-5p, miR-144-5p, let-7b-5p and let-7c-5p at baseline were assessed using qRT-PCR. Logistic regression was used to estimate the odds of developing MDD among individuals with different levels of miRNA expression.

RESULTS

Plasma expression levels of let-7b-5p were significantly lower (p = 0.02) at baseline in cases compared to controls. After adjustment for age and BMI, let-7b-5p was negatively associated with odds for developing MDD (OR = 0.33, p = 0.03, 95% CI = 0.12-0.91). Moreover, let-7b-5p expression levels showed a trend over time with larger differences between cases and controls for the earlier cases (MDD diagnosis <2 years from baseline) than MDD cases developed later (MDD diagnosis 2-5 years from baseline).

CONCLUSIONS

These findings show that lower plasma levels of let-7b-5p are associated with a higher future risk of MDD. Results need to be validated in a large cohort to examine its potential as a peripheral biomarker for MDD.

Intracellular calcium and inflammatory markers, mediated by purinergic stimulation, are differentially regulated in monocytes of patients with major depressive disorder

The P2X7 receptor (P2X7R) is a ligand-gated ion channel that is being recognized as a major player in neuropsychiatric disorders such as Major Depressive Disorder (MDD). P2X7R activation is triggered by high extracellular ATP concentrations, leading to channel opening and inducing an increase in cytosolic calcium concentration ([Ca²⁺]_c), that activates the inflammatory pathway. Those receptors are expressed not only in CNS cells but also in peripheral blood cells, where they are activated in response to inflammatory molecules such as bacterial lipopolysaccharide (LPS). LPS induced-tissue damage promotes an elevation of extracellular ATP, triggering the NRLP3-inflammasome assembly and activation that, sequentially, induces caspase-1 cleavage and IL-1β processing and secretion. In this context, we attempt to understand the role of P2X7R in [Ca²⁺]_c homeostasis regulation, inflammasome expression and its pharmacological modulation in MDD. For this purpose, monocytes were isolated from peripheral blood of MDD patients and [Ca²⁺]_c was monitored with the intracellular probe Fura-2. Our results point out to P2X7R as the responsible of the Ca²⁺ imbalance, as well as TNF-α-dependent activation of caspase-1 in MDD patients. In addition, P2X7R blockade with its specific antagonist, JNJ-47965567, reduces the Ca²⁺ entry upon Bz-ATP exposure. Altogether, our results point that MDD patients have both, Ca²⁺ homeostasis alteration and an inflammatory status, which promote an independent-inflammasome activation of caspase-1. Therefore, we propose the pharmacological modulation of P2X7R as a therapeutic approach against MDD symptoms.

MicroRNAs as Critical Biomarkers of Major Depressive Disorder: A Comprehensive Perspective

Major Depressive Disorder (MDD) represents a major global health concern, a body-mind malady of rising prevalence worldwide nowadays. The complex network of mechanisms involved in MDD pathophysiology is subjected to epigenetic changes modulated by microRNAs (miRNAs). Serum free or vesicles loaded miRNAs have starred numerous publications, denoting a key role in cell-cell communication, systematically and in brain structure and neuronal morphogenesis, activity and plasticity. Upregulated or downregulated expression of these signaling molecules may imply the impairment of genes implicated in pathways of MDD etiopathogenesis (neuroinflammation, brain-derived neurotrophic factor (BDNF), neurotransmitters, hypothalamic-pituitary-adrenal (HPA) axis, oxidative stress, circadian rhythms...). In addition, these miRNAs could serve as potential biomarkers with diagnostic, prognostic and predictive value, allowing to classify severity of the disease or to make decisions in clinical management. They have been considered as promising therapy targets as well and may interfere with available antidepressant treatments. As epigenetic malleable regulators, we also conclude emphasizing lifestyle interventions with physical activity, mindfulness and diet, opening the door to new clinical management considerations.

The miRNome of Depression

Depression is an effect of complex interactions between genetic, epigenetic and environmental factors. It is well established that stress responses are associated with multiple modest and often dynamic molecular changes in the homeostatic balance, rather than with a single genetic factor that has a strong phenotypic penetration. As depression is a multifaceted phenotype, it is important to study biochemical pathways that can regulate the overall allostasis of the brain. One such biological system that has the potential to fine-tune a multitude of diverse molecular processes is RNA interference (RNAi). RNAi is an epigenetic process showing a very low level of evolutionary diversity, and relies on the posttranscriptional regulation of gene expression using, in the case of mammals, primarily short (17–23 nucleotides) noncoding RNA transcripts called microRNAs (miRNA). In this review, our objective was to examine, summarize and discuss recent advances in the field of biomedical and clinical research on the role of miRNA-mediated regulation of gene expression in the development of depression. We focused on studies investigating post-mortem brain tissue of individuals with depression, as well as research aiming to elucidate the biomarker potential of miRNAs in depression and antidepressant response.

The role of circulating blood microRNA-374 and microRNA-10 levels in the pathogenesis and therapeutic mechanisms of major depressive disorder

Compelling recent evidence suggests that microRNAs (miRNAs) regulate specific mRNA transcripts at the transcriptomic level and coordinately influence complex regulatory networks, which may play a crucial role in the pathogenesis of major depressive disorder (MDD) and the treatment effects of antidepressants. To evaluate the possible involvement of miRNAs in the pathophysiology and therapeutic response of MDD, we conducted a miRNA expression array analysis of the peripheral blood mononuclear cells (PBMCs) of 5 depressed patients and 5 healthy controls (HCs). Subsequently, we chose 2 miRNAs for validation with real-time PCR (RT-PCR) analysis pre- and post-treatment in another group of 25 MDD patients and 25 HCs. In the array, 5 miRNAs were differentially expressed in medication-naïve MDD patients compared to HCs, of which 2 miRNAs were upregulated and 3 were downregulated. Furthermore, in comparison with HCs, MDD patients showed significantly lower expression levels of miR-374b and miR-10a before treatment. After 8 weeks of antidepressant treatment, both miR-374b and the miR-10a expression levels in MDD patients were significantly elevated only in responders. In conclusion, these results indicate the involvement of miR-374b and miR-10a in the biological mechanisms and therapeutic response of MDD, and provide new insights for exploring miRNAs as potential biomarkers for MDD.

Uncovering the Roles of MicroRNAs in Major Depressive Disorder: From Candidate Diagnostic Biomarkers to Treatment Response Indicators

Major depressive disorder (MDD) is a recurrent debilitating illness that represents a major health burden due to its increasing worldwide prevalence, unclear pathological mechanism, nonresponsive treatment, and lack of reliable and specific diagnostic biomarkers. Recently, microRNA species (miRs) have gained particular interest because they have the ability to post-transcriptionally regulate gene expression by modulating mRNA stability and translation in a cohesive fashion. By regulating entire genetic circuitries, miRs have been shown to have dysregulated expression levels in blood samples from MDD patients, when compared to healthy subjects. In addition, antidepressant treatment (AD) also appears to alter the expression pattern of several miRs. Therefore, we critically and systematically reviewed herein the studies assessing the potential biomarker role of several candidate miRs for MDD, as well as treatment response monitoring indicators, in order to enrich the current knowledge and facilitate possible diagnostic biomarker development for MDD, which could aid in reducing both patients' burden and open novel avenues toward a better understanding of MDD neurobiology.

Effective Antidepressant Chronotherapeutics (Sleep Deprivation and Light Therapy) Normalize the IL-1β:IL-1ra Ratio in Bipolar Depression

Background

Mood disorders associate with peripheral markers of low-grade inflammation, among which circulating levels of interleukin-1β (IL-1β) consistently predict diagnosis and poor outcomes. Antidepressant chronotherapeutics (total sleep deprivation and light therapy, TSD+LT) prompts response in drug-resistant bipolar depression, but its effect on peripheral inflammation were never assessed. Here we explored the effects of TSD+LT on IL-1β signaling.

Methods

We studied the ratio between IL-1β and its receptor antagonist (IL-1β:IL1ra) in 33 healthy participants, and in 26 inpatients with a major depressive episode in course of Bipolar Disorder, before and after treatment with three cycles of repeated TSD+LT, interspersed with sleep recovery nights, administered during 1 week. Treatment effects of mood and on IL-1β:IL1ra were analyzed in the context of the Generalized Linear Model (GLM).

Results

At baseline, patients had higher IL-1β, IL1ra, and IL-1β:IL1ra than controls. Treatment significantly decreased IL-1β:IL1ra, by decreasing IL-1β and increasing IL1ra, the effect being proportional to baseline levels and normalizing values. Patients with higher baseline levels showed the highest decrease in IL-1β:IL-1ra, which associated with the immediate antidepressant response at the first cycle; while patients with lower baseline values showed negligible changes in the IL-1β:IL-1ra, unrelated to treatment response.

Conclusion

We observed a parallel change of inflammatory biomarkers and severity of depression after chronotherapeutics, suggesting that a reduction in inflammation associated with depression could contribute to the mechanism of action of TSD+LT, and warranting interest for controlled studies addressing the role of inflammation in the recovery from bipolar depression.

Non-coding RNAs in depression: Promising diagnostic and therapeutic biomarkers

Non-coding RNAs (ncRNAs), including microRNAs, circular RNAs, and long non-coding RNAs, are important regulators of normal biological processes and their abnormal expression may be involved in the pathogenesis of human diseases including depression. Multiple studies have demonstrated a significantly increased or reduced ncRNAs expression in depressed patients compared with healthy subjects and that antidepressant therapy can alter the aberrant expression of ncRNAs in depressed patients. Although the existing evidence is important, it is also mixed and a comprehensive review to guide an effective clinical translation is lacking. Focused on human research, this review summarizes clinical findings of ncRNAs in depression, including those in brain tissues and peripheral samples. We outlined the characteristics and functions of ncRNAs and highlighted their performance in the diagnosis and treatment of depression. Although their precise roles in depression remain uncertain, ncRNAs have shown potential value as biomarkers for diagnosis and therapy in depressed patients.

Changes in RNA expression levels during antidepressant treatment: a systematic review

More than a third of patients treated with antidepressants experience treatment resistance. Furthermore, molecular pathways involved in antidepressant effect have yet to be fully understood. Therefore, we performed a systematic review of clinical studies that examined changes in RNA expression levels produced by antidepressant treatment. Literature search was performed through April 2021 for peer-reviewed studies measuring changes in mRNA or non-coding RNA levels before and after antidepressant treatment in human participants following PRISMA guidelines. Thirty-one studies were included in qualitative synthesis. We identified a large amount of heterogeneity between the studies for genes/RNAs measured, antidepressants used, and treatment duration. Of the six RNAs examined by more than one study, expression of the brain-derived neurotrophic factor (BDNF) gene and genes in the inflammation pathway, particularly IL-1β, were consistently reported to be altered by antidepressant treatment. Limitations of this review include heterogeneity of the studies, possibility of positive publication bias, and risk of false-negative findings secondary to small sample sizes. In conclusion, our systematic review provides an updated synthesis of RNA expression changes produced by antidepressant treatment in human participants, where genes in the BDNF and inflammatory pathways were identified as potential targets of antidepressant effect. Importantly, these findings also highlight the need for replication of the included studies in multiple strong, placebo-controlled studies for the identification of evidence-based markers that can be targeted to improve treatment outcomes.

Biochemical Correlates of Video Game Use: From Physiology to Pathology. A Narrative Review

In the last few decades, video game playing progressively became a widespread activity for many people, in childhood as well in adulthood. An increasing amount of literature has focused on pathological and non-pathological correlates of video game playing, with specific attention towards Internet Gaming Disorder (IGD). While many neurobiological studies in this field were based on neuroimaging, highlighting structural and functional brain changes among video game users, only a limited number of studies investigated the presence of biochemical correlates of video gaming. The present work aims to summarize and review the available literature about biochemical changes linked to video game use in IGD patients as well as non-pathological users, and the differences in between. Results may shed light on risks and benefits of video games, providing directions for further research on IGD treatment and, on other hand, on the potential role of video games in therapeutic or preventive protocols for specific conditions.

MicroRNAs, Multiple Sclerosis, and Depression

Multiple sclerosis (MS) is a chronic disease of the central nervous system that affects the brain and spinal cord. There are several disease courses in MS including relapsing–remitting MS (RRMS), primary progressive MS (PPMS), and secondary progressive MS (SPMS). Up to 50% of MS patients experience depressive disorders. Major depression (MD) is a serious comorbidity of MS. Many dysfunctions including neuroinflammation, peripheral inflammation, gut dysbiosis, chronic oxidative and nitrosative stress, and neuroendocrine and mitochondrial abnormalities may contribute to the comorbidity between MS and MD. In addition to these actions, medical treatment and microRNA (miRNA) regulation may also be involved in the mechanisms of the comorbidity between MS and MD. In the study, I review many common miRNA biomarkers for both diseases. These common miRNA biomarkers may help further explore the association between MS and MD.

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Background

Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.

Materials and Methods

We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.

Results

Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.

Conclusion

This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Alterations in circulating extracellular vesicles underlie social stress-induced behaviors in mice

Chronic stress induces peripheral and intracerebral immune changes and inflammation, contributing to neuropathology and behavioral abnormalities relevant to psychiatric disorders such as depression. Although the pathological implication of many peripheral factors such as pro‐inflammatory cytokines, hormones, and macrophages has been demonstrated, the roles of circulating extracellular vesicles (EVs) for chronic stress mechanisms remain poorly investigated. Here, we report that chronic social defeat stress (CSDS)‐induced social avoidance phenotype, assessed by a previously untested three‐chamber social approach test, can be distinguished by multiple pro‐inflammatory cytokines and EV‐associated molecular signatures in the blood. We found that the expression patterns of miRNAs distinguished the CSDS‐susceptible mice from the CSDS‐resilient mice. Social avoidance behavior scores were also estimated with good accuracy by the expression patterns of multiple EV‐associated miRNAs. We also demonstrated that EVs enriched from the CSDS‐susceptible mouse sera upregulated the production of pro‐inflammatory cytokines in the LPS‐stimulated microglia‐like cell lines. Our results indicate the role of circulating EVs and associated miRNAs in CSDS susceptibility, which may be related to pro‐inflammatory mechanisms underlying stress‐induced neurobehavioral outcomes.

Emerging role of microRNAs in major depressive disorder and its implication on diagnosis and therapeutic response

BACKGROUND

Major depressive disorder (MDD) is a serious and common psychiatric disorder with a high prevalence in the population. Although great advances have been made, its pathogenesis is still unclear and a validated biomarker for diagnosis or therapeutic response remains unidentified. This review aims at summarizing the functional role of miRNAs in MDD pathogenesis and their potential as biomarkers for MDD diagnosis and antidepressant response.

METHODS

We performed a bibliographic research on the main databases (PubMed, Google Scholar and Web of Science) using the terms "microRNAs", "major depressive disorder", "synaptic plasticity", "biomarker", "antidepressant treatment", in order to find studies that propose the role of microRNAs in MDD pathogenesis and their potential as biomarkers for MDD diagnosis and antidepressant response.

RESULTS

microRNAs (miRNAs), a class of small noncoding RNAs, act as key regulators of synaptic plasticity in MDD pathogenesis. Growing researches provide the evidence for peripheral miRNAs as potential biomarkers for MDD diagnosis and antidepressant response. These results suggest that targeting miRNAs directly could be therapeutically beneficial for MDD and miRNAs are potential biomarkers of MDD and its treatment.

LIMITATIONS

The role of miRNAs in MDD pathogenesis needs further investigation. Whether miRNAs in peripheral tissues truly represent brain-derived miRNAs is still unclear at the present time. Moreover, only a few blood miRNAs alterations are consistent across studies.

CONCLUSIONS

Overall, miRNAs act key regulators of synaptic plasticity in MDD pathogenesis and hold significant promise as biomarkers or therapeutic targets for MDD, but further research is still needed.